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Description: 独立成分分析
目前，独立成分分析(ICA)这门新的数据处理技术已得到了国内外学术界的普遍认可和广泛关注，并在不同的研究和应用领域开始发挥重要作用。ICA这一专业术语及其内涵，“鸡尾酒会”问题、脑电信号分离、通信系统的盲分离等ICA的典型应用问题，以及ICA中有关峭度、不动点算法等一系列基本概念，已为国内许多从事数据处理的科研工作者所熟悉。但是，要了解独立成分分析背后所隐含的重要应用背景与数学理论基础；把握ICA的应用范围及相应的各种限制；了解如何应用独立成分分析的思路，从而针对具体的专业应用问题提出更为合理的分析方法，这些更深层次的内容，可能并非通过阅读几篇综述文献或相关专业文献，或学习、分析和使用MATLAB工具包中的那些现成算法就能很好地掌握的。然而，目前国内仍然没有一本比较全面而深入的中文教材来供那些对ICA感兴趣的初学者和不同专业领域的研究人员做参考。这种状况下，翻译出版一本比较权威的ICA教材就显得很有必要。本书的英文版是国际上第一本对ICA这门新技术进行全面介绍的综合性专著，作者Aapo Hyv妑inen，Juha Karhunen和Erikki Oja为ICA方法的建立做出过重要贡献，并且一直活跃在ICA的相关研究领域。本书的内容主要取材于作者自己的研究小组所开展的-Independent component analysis
Currently, independent component analysis (ICA) which door the new data-processing technology has been widely recognized by domestic and foreign academic and attention, and different studies and applications play an important role began. ICA and its connotation of the terminology, "cocktail party" problem, EEG separation, communication systems for blind separation of a typical application of ICA, and ICA in the kurtosis, fixed point algorithms and a series of fundamental concepts, has been Many engage in data processing for the domestic workers are familiar with the research. However, independent component analysis to understand the importance of applications on the underlying mathematical background and theory grasp the application of ICA and the corresponding limitations know how to apply the idea of independent component analysis to specific application of a more professional reasonable methods of analysis, these deeper levels of content, may not be
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Author: houhj |
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Description: eeg的脑电地形图，用于matlab中处理eeg数据。-eeg topographic maps of EEG for addressing eeg matlab data.
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Author: 范范 |
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Description: 应用多变量相空间重构对分析脑电信号Multivariate analysis of the phase space reconstruction of the EEG-Multivariate analysis of the phase space reconstruction of the EEGMultivariate analysis of the phase space reconstruction of the EEG
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Description: SSVEP脑电分析算法，计算脑电的频率和相位信息，并做出判断-SSVEP EEG analysis algorithm to calculate EEG frequency and phase information, and make judgments
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Author: 何云 |
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Description: edf reading for eeg using for biomedical engineer
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Author: mohammad dardouna |
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Description: REMOVAL OF NOISE FROM ECG (ELECTROCARDIOGRAPHY) BY USING MATLAB.
EEG (Electroencephalograph) recording from the scalp has biological artifacts and external artifacts. Biological artifacts, which are generated, can be EMG (Electromyography) signal, EOG (Electrooculograph) signal or ECG (Electrocardiograph) signal. These artifacts appear as noise in the recorded EEG signal individually or in a combined manner. Usually physicians are misled by these noisy signals and the EEG analysis can go wrong. This paper presents noise cancellation i.e. removal of noise signal which can be either EMG, ECG or a combination of these two artifacts from the corrupted EEG signal and also signal enhancement both using recurrent learning technique. For this purpose, we have implemented the RTRL (Real Time Recurrent Learning) algorithm,
-REMOVAL OF NOISE FROM ECG (ELECTROCARDIOGRAPHY) BY USING MATLAB.
EEG (Electroencephalograph) recording from the scalp has biological artifacts and external artifacts. Biological artifacts, which are generated, can be EMG (Electromyography) signal, EOG (Electrooculograph) signal or ECG (Electrocardiograph) signal. These artifacts appear as noise in the recorded EEG signal individually or in a combined manner. Usually physicians are misled by these noisy signals and the EEG analysis can go wrong. This paper presents noise cancellation i.e. removal of noise signal which can be either EMG, ECG or a combination of these two artifacts from the corrupted EEG signal and also signal enhancement both using recurrent learning technique. For this purpose, we have implemented the RTRL (Real Time Recurrent Learning) algorithm,
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Author: azharuddin |
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Description: 对脑电信号进行滤波、快速傅里叶变换（FFT）及自相关、互相关计算，以观察每一通道脑电的相关性-On EEG filtering, fast Fourier transform (FFT) and autocorrelation, cross correlation calculation, in order to observe the correlation of each channel EEG
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Author: 王荣 |
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Description: eeg数据处理,此代码为美国ucsd脑科学实验室开发的著名实验程序。-eeg data processing
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Author: yanping |
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Description: EEG FEATURE EXTRACTION
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Author: rajshree |
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Description: eeg detection whether normal or abnormal
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Size: 4096 |
Author: anand pawar |
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Description: These functions have been developed to watch and analyze EEG/Video/Accelero(3A)/Magneto(3M) data in the context of the Epimouv/Capametrim project.
The project aim is to recognized epileptic movement disorders by determining regions of interests on the video and classify motor manifestations thanks to 3A3M sensors.
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Author: 12pm |
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Description: code to decode raw eeg data..eeg sample matrices
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Author: radhika |
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Description: simulation of raw eeg signal.
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Author: radhika |
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Description: EEG signal classification using wavelet feature extraction
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Author: vipul |
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Description: EEG signal classification using wavelet feature extraction
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Author: vipul |
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Description: EEG signal classification using wavelet feature extraction
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Author: vipul |
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Description: EEG signal classification using wavelet feature extraction
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Size: 19456 |
Author: vipul |
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Description: EEG signal classification using wavelet feature extraction
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Author: vipul |
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Description: EEG-Based Lapse Detection With High Temporal resolution
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Author: Hasan |
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Description: this about the brain computer interface using labview.the eeg signals are simulated and viewed here there by comuniating human brain to the computer
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Author: archana |
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